-MSA — A GPU-based, fast and accurate algorithm for multiple sequence alignment

Błażewicz, Jacek; Frohmberg, Wojciech; Kierzynka, Michał; Wojciechowski, Pawel

doi:10.1016/j.jpdc.2012.04.004

Cited by 45 publications

(19 citation statements)

References 27 publications

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“…GPU accelerated T-Coffee called G-MSA [23] is evaluated; its performance is reported only for overall execution time since it's designed with conjunction of software multithreading. The dataset for MSA evaluation is the BAliBase v3 [57] benchmark set.…”

Section: Test Resultsmentioning

confidence: 99%

See 1 more Smart Citation

3D-Stacked Many-Core Architecture for Biological Sequence Analysis Problems

Liu

Hemani

Paul

et al. 2017

Int J Parallel Prog

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Sequence analysis plays extremely important role in bioinformatics, and most applications of which have compute intensive kernels consuming over 70% of total execution time. By exploiting the compute intensive execution stages of popular sequence analysis applications, we present and evaluate a VLSI architecture with a focus on those that target at biological sequences directly, including pairwise sequence alignment, multiple sequence alignment, database search, and short read sequence mappings. Based on coarse grained reconfigurable array we propose the use of many-core and 3D-stacked technologies to gain further improvement over memory subsystem, which gives another order of magnitude speedup from high bandwidth and low access latency. We analyze our approach in terms of its throughput and efficiency for different application mappings. Initial experimental results are evaluated from a stripped down implementation in a commodity FPGA, and then we scale the results to estimate the performance of our architecture with 9 layers of 70 mm 2 stacked wafers in 45-nm process. We demonstrate numerous estimated speedups better than corresponding existed hardware accelerator platforms for at least 40 times for the entire range of applications and datasets of interest. In comparison, the alternative FPGA based accelerators deliver only improvement for single application, while GPGPUs perform not well enough on accelerating program kernel with random memory access and integer addition/comparison operations.

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Section: Test Resultsmentioning

confidence: 99%

“…For MSA problem, accelerating with FPGA has been well researched [7][8][9][10], while recent reports using GPU as accelerator gave very impressive results [22,23].…”

Section: Related Workmentioning

confidence: 99%

3D-Stacked Many-Core Architecture for Biological Sequence Analysis Problems

Liu

Hemani

Paul

et al. 2017

Int J Parallel Prog

View full text Add to dashboard Cite

show abstract

“…Even though there are some GPU-based tools that support similar scenarios [1], they were designed to deal with other problems, e.g. MSA [2]. Our brief estimation of the computational time of such an algorithm applied for real-life reads mapping scanario was that this would take a few years.…”

Section: Discussionmentioning

confidence: 99%

G-MAPSEQ – a new method for mapping reads to a reference genome

Wojciechowski¹,

Frohmberg²,

Kierzynka³

et al. 2016

Foundations of Computing and Decision Sciences

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Abstract. The problem of reads mapping to a reference genome is one of the most essential problems in modern computational biology. The most popular algorithms used to solve this problem are based on the Burrows-Wheeler transform and the FM-index. However, this causes some issues with highly mutated sequences due to a limited number of mutations allowed. G-MAPSEQ is a novel, hybrid algorithm combining two interesting methods: alignment-free sequence comparison and an ultra fast sequence alignment. The former is a fast heuristic algorithm which uses k-mer characteristics of nucleotide sequences to find potential mapping places. The latter is a very fast GPU implementation of sequence alignment used to verify the correctness of these mapping positions. The source code of G-MAPSEQ along with other bioinformatic software is available at: http://gpualign.cs.put.poznan.pl.

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“…This means that their performance drops dramatically if applied to the scheme described in the introductory section, in which only selected pairs of sequences are aligned. On the other hand, there are also some interesting GPU tools on the market that perform pairwise sequence alignment with the backtracking step [21,22], sequence mapping [23] or even multiple sequence alignment [24]. Yet, none of them has been suitable to be used effectively in the next-generation de-novo assembly problem.…”

Section: Related Workmentioning

confidence: 99%

G-DNA – a highly efficient multi-GPU/MPI tool for aligning nucleotide reads

Frohmberg¹,

Kierzynka²,

Błażewicz³

et al. 2013

Bulletin of the Polish Academy of Sciences: Technical Sciences

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Abstract. DNA/RNA sequencing has recently become a primary way researchers generate biological data for further analysis. Assembling algorithms are an integral part of this process. However, some of them require pairwise alignment to be applied to a great deal of reads. Although several efficient alignment tools have been released over the past few years, including those taking advantage of GPUs (Graphics Processing Units), none of them directly targets high-throughput sequencing data. As a result, a need arose to create software that could handle such data as effectively as possible. G-DNA (GPU-based DNA aligner) is the first highly parallel solution that has been optimized to process nucleotide reads (DNA/RNA) from modern sequencing machines. Results show that the software reaches up to 89 GCUPS (Giga Cell Updates Per Second) on a single GPU and as a result it is the fastest tool in its class. Moreover, it scales up well on multiple GPUs systems, including MPI-based computational clusters, where its performance is counted in TCUPS (Tera CUPS).

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-MSA — A GPU-based, fast and accurate algorithm for multiple sequence alignment

Cited by 45 publications

References 27 publications

3D-Stacked Many-Core Architecture for Biological Sequence Analysis Problems

3D-Stacked Many-Core Architecture for Biological Sequence Analysis Problems

G-MAPSEQ – a new method for mapping reads to a reference genome

G-DNA – a highly efficient multi-GPU/MPI tool for aligning nucleotide reads

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